{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5c6f283a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from run_batch import run_batch\n",
    "import glob\n",
    "from run_batch_broker import run_batch_with_broker\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8529d9e2",
   "metadata": {},
   "source": [
    "## Polarization Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f54de6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1/100] seed=3967\n",
      "[2/100] seed=9012\n",
      "[3/100] seed=1499\n",
      "[4/100] seed=6323\n",
      "[5/100] seed=2952\n",
      "[6/100] seed=6837\n",
      "[7/100] seed=1923\n",
      "[8/100] seed=493\n",
      "[9/100] seed=2283\n",
      "[10/100] seed=3759\n",
      "[11/100] seed=4363\n",
      "[12/100] seed=8114\n",
      "[13/100] seed=527\n",
      "[14/100] seed=7167\n",
      "[15/100] seed=1886\n",
      "[16/100] seed=5193\n",
      "[17/100] seed=2048\n",
      "[18/100] seed=1682\n",
      "[19/100] seed=467\n",
      "[20/100] seed=7906\n",
      "[21/100] seed=3172\n",
      "[22/100] seed=6563\n",
      "[23/100] seed=84\n",
      "[24/100] seed=5833\n",
      "[25/100] seed=8185\n",
      "[26/100] seed=2513\n",
      "[27/100] seed=6475\n",
      "[28/100] seed=2773\n",
      "[29/100] seed=2896\n",
      "[30/100] seed=3025\n",
      "[31/100] seed=894\n",
      "[32/100] seed=334\n",
      "[33/100] seed=1086\n",
      "[34/100] seed=7521\n",
      "[35/100] seed=5681\n",
      "[36/100] seed=3143\n",
      "[37/100] seed=9841\n",
      "[38/100] seed=2291\n",
      "[39/100] seed=6319\n",
      "[40/100] seed=2275\n",
      "[41/100] seed=2489\n",
      "[42/100] seed=5578\n",
      "[43/100] seed=2434\n",
      "[44/100] seed=9207\n",
      "[45/100] seed=6117\n",
      "[46/100] seed=763\n",
      "[47/100] seed=8540\n",
      "[48/100] seed=7797\n",
      "[49/100] seed=6767\n",
      "[50/100] seed=7141\n",
      "[51/100] seed=9886\n",
      "[52/100] seed=1057\n",
      "[53/100] seed=8778\n",
      "[54/100] seed=9144\n",
      "[55/100] seed=3049\n",
      "[56/100] seed=3112\n",
      "[57/100] seed=6750\n",
      "[58/100] seed=7173\n",
      "[59/100] seed=7469\n",
      "[60/100] seed=8626\n",
      "[61/100] seed=9363\n",
      "[62/100] seed=3698\n",
      "[63/100] seed=4076\n",
      "[64/100] seed=3117\n",
      "[65/100] seed=4548\n",
      "[66/100] seed=739\n",
      "[67/100] seed=6603\n",
      "[68/100] seed=3870\n",
      "[69/100] seed=6130\n",
      "[70/100] seed=1053\n",
      "[71/100] seed=3034\n",
      "[72/100] seed=8067\n",
      "[73/100] seed=1152\n",
      "[74/100] seed=3451\n",
      "[75/100] seed=1572\n",
      "[76/100] seed=6583\n",
      "[77/100] seed=3944\n",
      "[78/100] seed=1360\n",
      "[79/100] seed=3793\n",
      "[80/100] seed=2406\n",
      "[81/100] seed=7083\n",
      "[82/100] seed=7962\n",
      "[83/100] seed=5420\n",
      "[84/100] seed=7425\n",
      "[85/100] seed=1205\n",
      "[86/100] seed=2230\n",
      "[87/100] seed=385\n",
      "[88/100] seed=7097\n",
      "[89/100] seed=3350\n",
      "[90/100] seed=8266\n",
      "[91/100] seed=4250\n",
      "[92/100] seed=2883\n",
      "[93/100] seed=1811\n",
      "[94/100] seed=5798\n",
      "[95/100] seed=3351\n",
      "[96/100] seed=7900\n",
      "[97/100] seed=3676\n",
      "[98/100] seed=4084\n",
      "[99/100] seed=9859\n",
      "[100/100] seed=1100\n"
     ]
    }
   ],
   "source": [
    "run_batch(\n",
    "    num_runs=100,\n",
    "    broker_type='baseline',\n",
    "    model_type_base='er',\n",
    "    total_iterations=5000,\n",
    "    n=200,\n",
    "    network_type='Gnp',\n",
    "    k=8,\n",
    "    p=0.1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "60959985",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use a clean grid background\n",
    "plt.style.use('seaborn-whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8663e55",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_pattern = 'power_er_run*.csv'\n",
    "files = sorted(glob.glob(file_pattern))\n",
    "\n",
    "# Ensure we actually found the files\n",
    "if not files:\n",
    "    raise FileNotFoundError(f\"No files matching {file_pattern} in {os.getcwd()}\")\n",
    "\n",
    "all_runs = []\n",
    "for idx, fpath in enumerate(files, start=1):\n",
    "    df = pd.read_csv(fpath)\n",
    "    df['run'] = idx\n",
    "    all_runs.append(df)\n",
    "\n",
    "all_df = pd.concat(all_runs, ignore_index=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e94fc8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_df = (\n",
    "    all_df\n",
    "    .groupby('Iteration')['Assortativity']\n",
    "    .mean()\n",
    "    .reset_index()\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8c449863",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "# Plot each run with semi‐transparent lines\n",
    "for run_id, grp in all_df.groupby('run'):\n",
    "    ax.plot(\n",
    "        grp['Iteration'],\n",
    "        grp['Assortativity'],\n",
    "        linewidth=1,\n",
    "        alpha=0.3\n",
    "    )\n",
    "\n",
    "# Overlay the mean curve in a thick black line\n",
    "ax.plot(\n",
    "    mean_df['Iteration'],\n",
    "    mean_df['Assortativity'],\n",
    "    color='black',\n",
    "    linewidth=3,\n",
    "    label='Mean'\n",
    ")\n",
    "\n",
    "# Labels, title, legend\n",
    "ax.set_xlabel('Iteration')\n",
    "ax.set_ylabel('Assortativity')\n",
    "ax.set_title('Spaghetti Plot of Assortativity Across Runs')\n",
    "ax.legend()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fa3dd8a",
   "metadata": {},
   "source": [
    "## Broker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c81c098c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1/100] seed=5839\n",
      "Power Broker added at iteration 2575\n",
      "[2/100] seed=6369\n",
      "Power Broker added at iteration 3100\n",
      "[3/100] seed=5153\n",
      "Power Broker added at iteration 2925\n",
      "[4/100] seed=5542\n",
      "Power Broker added at iteration 2325\n",
      "[5/100] seed=7943\n",
      "Power Broker added at iteration 2925\n",
      "[6/100] seed=22\n",
      "Power Broker added at iteration 3100\n",
      "[7/100] seed=2311\n",
      "Power Broker added at iteration 3150\n",
      "[8/100] seed=8270\n",
      "Power Broker added at iteration 2750\n",
      "[9/100] seed=5763\n",
      "Power Broker added at iteration 2975\n",
      "[10/100] seed=6574\n",
      "Power Broker added at iteration 2850\n",
      "[11/100] seed=211\n",
      "Power Broker added at iteration 2800\n",
      "[12/100] seed=5415\n",
      "Power Broker added at iteration 3025\n",
      "[13/100] seed=9146\n",
      "Power Broker added at iteration 2625\n",
      "[14/100] seed=4108\n",
      "Power Broker added at iteration 2775\n",
      "[15/100] seed=5015\n",
      "Power Broker added at iteration 2700\n",
      "[16/100] seed=6164\n",
      "Power Broker added at iteration 3025\n",
      "[17/100] seed=2323\n",
      "Power Broker added at iteration 2925\n",
      "[18/100] seed=4957\n",
      "Power Broker added at iteration 2825\n",
      "[19/100] seed=30\n",
      "Power Broker added at iteration 2575\n",
      "[20/100] seed=4963\n",
      "Power Broker added at iteration 2625\n",
      "[21/100] seed=7551\n",
      "Power Broker added at iteration 3225\n",
      "[22/100] seed=7825\n",
      "Power Broker added at iteration 2675\n",
      "[23/100] seed=3201\n",
      "Power Broker added at iteration 2650\n",
      "[24/100] seed=1319\n",
      "Power Broker added at iteration 2975\n",
      "[25/100] seed=6934\n",
      "Power Broker added at iteration 3025\n",
      "[26/100] seed=6314\n",
      "Power Broker added at iteration 3300\n",
      "[27/100] seed=8472\n",
      "Power Broker added at iteration 3075\n",
      "[28/100] seed=7204\n",
      "Power Broker added at iteration 3225\n",
      "[29/100] seed=2105\n",
      "Power Broker added at iteration 2900\n",
      "[30/100] seed=7187\n",
      "Power Broker added at iteration 2950\n",
      "[31/100] seed=2410\n",
      "Power Broker added at iteration 2600\n",
      "[32/100] seed=9606\n",
      "Power Broker added at iteration 2650\n",
      "[33/100] seed=997\n",
      "Power Broker added at iteration 2775\n",
      "[34/100] seed=5690\n",
      "Power Broker added at iteration 3175\n",
      "[35/100] seed=3643\n",
      "Power Broker added at iteration 3325\n",
      "[36/100] seed=3292\n",
      "Power Broker added at iteration 2625\n",
      "[37/100] seed=8107\n",
      "Power Broker added at iteration 2800\n",
      "[38/100] seed=3517\n",
      "Power Broker added at iteration 2375\n",
      "[39/100] seed=2403\n",
      "Power Broker added at iteration 2900\n",
      "[40/100] seed=4515\n",
      "Power Broker added at iteration 2850\n",
      "[41/100] seed=3807\n",
      "Power Broker added at iteration 2875\n",
      "[42/100] seed=1340\n",
      "Power Broker added at iteration 2950\n",
      "[43/100] seed=3699\n",
      "Power Broker added at iteration 2900\n",
      "[44/100] seed=5895\n",
      "Power Broker added at iteration 2675\n",
      "[45/100] seed=7602\n",
      "Power Broker added at iteration 2950\n",
      "[46/100] seed=6977\n",
      "Power Broker added at iteration 2825\n",
      "[47/100] seed=9609\n",
      "Power Broker added at iteration 2800\n",
      "[48/100] seed=7487\n",
      "Power Broker added at iteration 2875\n",
      "[49/100] seed=7875\n",
      "Power Broker added at iteration 2575\n",
      "[50/100] seed=2768\n",
      "Power Broker added at iteration 3050\n",
      "[51/100] seed=5479\n",
      "Power Broker added at iteration 2950\n",
      "[52/100] seed=9622\n",
      "Power Broker added at iteration 3150\n",
      "[53/100] seed=6845\n",
      "Power Broker added at iteration 3025\n",
      "[54/100] seed=8972\n",
      "Power Broker added at iteration 2600\n",
      "[55/100] seed=8950\n",
      "Power Broker added at iteration 2925\n",
      "[56/100] seed=489\n",
      "Power Broker added at iteration 3025\n",
      "[57/100] seed=6128\n",
      "Power Broker added at iteration 2400\n",
      "[58/100] seed=5114\n",
      "Power Broker added at iteration 2725\n",
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      "[100/100] seed=337\n",
      "Power Broker added at iteration 2900\n"
     ]
    }
   ],
   "source": [
    "run_batch_with_broker(\n",
    "    num_runs=100,\n",
    "    broker_type='power',\n",
    "    total_iterations=5000,\n",
    "    model_type_base='er',\n",
    "    n=200,\n",
    "    network_type='Gnp',\n",
    "    k=8,\n",
    "    p=0.1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aba59c58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1/100] seed=8389\n",
      "Structural Broker added at iteration 3075\n",
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      "Structural Broker added at iteration 2675\n",
      "[100/100] seed=2143\n",
      "Structural Broker added at iteration 2950\n"
     ]
    }
   ],
   "source": [
    "run_batch_with_broker(\n",
    "    num_runs=100,\n",
    "    broker_type='structural',\n",
    "    total_iterations=5000,\n",
    "    model_type_base='er',\n",
    "    n=200,\n",
    "    network_type='Gnp',\n",
    "    k=8,\n",
    "    p=0.1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "95190eff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1/100] seed=1414\n",
      "Social Broker added at iteration 2950\n",
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      "Social Broker added at iteration 3050\n",
      "[3/100] seed=45\n",
      "Social Broker added at iteration 2725\n",
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      "Social Broker added at iteration 2500\n",
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      "Social Broker added at iteration 3475\n",
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      "Social Broker added at iteration 2600\n",
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      "Social Broker added at iteration 2675\n",
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      "Social Broker added at iteration 2575\n",
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      "Social Broker added at iteration 2650\n",
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      "Social Broker added at iteration 3050\n",
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      "Social Broker added at iteration 3250\n",
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      "Social Broker added at iteration 3125\n",
      "[85/100] seed=7762\n",
      "Social Broker added at iteration 2825\n",
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      "Social Broker added at iteration 2800\n",
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      "Social Broker added at iteration 2725\n",
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      "Social Broker added at iteration 2925\n",
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      "Social Broker added at iteration 2700\n",
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      "Social Broker added at iteration 2700\n",
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      "Social Broker added at iteration 2775\n",
      "[94/100] seed=8351\n",
      "Social Broker added at iteration 3075\n",
      "[95/100] seed=945\n",
      "Social Broker added at iteration 3300\n",
      "[96/100] seed=6339\n",
      "Social Broker added at iteration 3000\n",
      "[97/100] seed=2247\n",
      "Social Broker added at iteration 3325\n",
      "[98/100] seed=2829\n",
      "Social Broker added at iteration 3025\n",
      "[99/100] seed=9926\n",
      "Social Broker added at iteration 2800\n",
      "[100/100] seed=7357\n",
      "Social Broker added at iteration 2850\n"
     ]
    }
   ],
   "source": [
    "run_batch_with_broker(\n",
    "    num_runs=100,\n",
    "    broker_type='social',\n",
    "    total_iterations=5000,\n",
    "    model_type_base='er',\n",
    "    n=200,\n",
    "    network_type='Gnp',\n",
    "    k=8,\n",
    "    p=0.1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1dda3fc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1/100] seed=2399\n",
      "Optimal Broker added at iteration 2675\n",
      "[2/100] seed=3790\n",
      "Optimal Broker added at iteration 2750\n",
      "[3/100] seed=9606\n",
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      "Optimal Broker added at iteration 3000\n",
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      "Optimal Broker added at iteration 2725\n",
      "[100/100] seed=5119\n",
      "Optimal Broker added at iteration 2925\n"
     ]
    }
   ],
   "source": [
    "run_batch_with_broker(\n",
    "    num_runs=100,\n",
    "    broker_type='optimal',\n",
    "    total_iterations=5000,\n",
    "    model_type_base='er',\n",
    "    n=200,\n",
    "    network_type='Gnp',\n",
    "    k=8,\n",
    "    p=0.1\n",
    ")"
   ]
  }
 ],
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